A novel AI-powered method for robust identification of operational phases in refrigerators

Refrigerators are essential appliances in our daily lives, while the accurate identification of their operational phase is crucial for various applications, such as energy management and maintenance. This paper introduces a novel method to predict the operational phase of refrigerators based on power consumption data, exploiting artificial intelligence (AI) and pattern recognition techniques. Specifically, the proposed method initially identifies the driving speed of the compressor (fixed or variable speed) utilizing an Adaptive Boosting, (AdaBoost) classifier. Following that, a Long Short-Term Memory (LSTM) classifier was designed for each type of compressor based on extracted statistical features. This classifier is used to categorize daily power consumption into the different operational phases. Moreover, the proposed AI-driven diagnostic tool facilitates operational-based prescriptive maintenance by addressing periodic and phase-specific maintenance actions while providing homeowners with informed suggestions on alternative refrigerator models that offer greater cost savings and reduced annual energy consumption. The proposed method was tested on real and simulated data for over 20 years, with the AdaBoost classifier achieving an accuracy close to 97% and an F1 score close to 97% and the LSTM nodes achieving an accuracy of approximately 95% in all cases. Finally, the method demonstrated robustness when applied to new datasets, showing comparable performance on unknown data, rendering it a powerful tool for refrigerator management and maintenance.